Domain Adaptation

Most of the theoretical analysis of machine learning classification tasks addresses
a setup in which the training and test data are randomly generated by
the same data distribution. While this may
be a good approximation of reality in some machine learning tasks, in many practical applications
this assumption cannot be jutified. The data-generating distribution might change over time or there might simply not be any labeled data available from the relevant target domain
to train a classifier on. The task of learning when the training data is generated differently than the data one aims to predict is referred to as Domain Adaption (DA) learning.
Domain Adaptation tasks occur in many practical situations and are frequently addressed in experimental research,
however, achieving a solid theoretical understanding of remains a challenge.